MACE: A Flexible Framework for Membership Privacy Estimation in Generative Models. (arXiv:2009.05683v3 [cs.CR] UPDATED)

In this work, we formally study the membership privacy risk of generative
models and propose a membership privacy estimation framework. We formulate the
membership privacy risk as a statistical divergence between training samples
and hold-out samples, and propose sample-based methods to estimate this
divergence. Unlike previous works, our proposed metric and estimators make
realistic and flexible assumptions. First, we offer a generalizable metric as
an alternative to accuracy for imbalanced datasets. Second, our estimators are
capable of estimating the membership privacy risk given any scalar or vector
valued attributes from the learned model, while prior work require access to
specific attributes. This allows our framework to provide data-driven
certificates for trained generative models in terms of membership privacy risk.
Finally, we show a connection to differential privacy, which allows our
proposed estimators to be used to understand the privacy budget ‘epsilon’
needed for differentially private generative models. We demonstrate the utility
of our framework through experimental demonstrations on different generative
models using various model attributes yielding some new insights about
membership leakage and vulnerabilities of models.

MACE: A Flexible Framework for Membership Privacy Estimation in Generative Models. (arXiv:2009.05683v3 [cs.CR] UPDATED)

In this work, we formally study the membership privacy risk of generative
models and propose a membership privacy estimation framework. We formulate the
membership privacy risk as a statistical divergence between training samples
and hold-out samples, and propose sample-based methods to estimate this
divergence. Unlike previous works, our proposed metric and estimators make
realistic and flexible assumptions. First, we offer a generalizable metric as
an alternative to accuracy for imbalanced datasets. Second, our estimators are
capable of estimating the membership privacy risk given any scalar or vector
valued attributes from the learned model, while prior work require access to
specific attributes. This allows our framework to provide data-driven
certificates for trained generative models in terms of membership privacy risk.
Finally, we show a connection to differential privacy, which allows our
proposed estimators to be used to understand the privacy budget ‘epsilon’
needed for differentially private generative models. We demonstrate the utility
of our framework through experimental demonstrations on different generative
models using various model attributes yielding some new insights about
membership leakage and vulnerabilities of models.